Jennifer Lopes

📚 Livros

Última atualização: 22/08/2025

Sumário

📘Machine Learning

📘 Deep Learning

📘 LLMs, RAG e IA Generativa

📘 Estatística

📘 Programação em R

📘 Machine Learning

Título Link
1 Aprendizado de Máquina: Uma Abordagem Estatística – Izbicki & Santos [Link]
2 Inteligência Artificial e Aprendizagem de Máquina – Oscar Gabriel Filho [Link]
3 Introdução ao Aprendizado de Máquina para Análise de Sobrevivência – Ara et al. [Link]
4 Uma Introdução à Machine Learning – David Menotti (UFPR) [Link]
5 A Course in Machine Learning – Hal Daumé III [Link]
6 Mathematics for Machine Learning – Beginners – Mueller & Massaron [Link]
7 ML Cheatsheet for Beginners – Jupyter Compilation [Link]
8 Decision Tree Regressor Basics – Nikita Prasad [Link]
9 Tutorials on Machine Learning & Deep Learning – Compilação técnica [Link]
10 Algorithmic Mathematics in Machine Learning – Bohn, Garcke, Griebel [Link]
11 Engineering MLOps – Emmanuel Raj [Link]
12 Interpretable Machine Learning with Python – Serg Masís [Link]
13 Machine Learning Applications: From Computer Vision to Robotics – Chatterjee & Zalte (Eds.) [Link]
14 Machine Learning Using R – Brett Lantz [Link]
15 A Minimal rTorch Book https://f0nzie.github.io/rtorch-minimal-book/
16 Applied Machine Learning for Tabular Data https://aml4td.org/
17 Applied Machine Learning Using mlr3 in R https://mlr3book.mlr-org.com/
18 Behavior Analysis with Machine Learning Using R https://enriquegit.github.io/behavior-free/index.html#
19 Data Science: Theories, Models, Algorithms, and Analytics https://srdas.github.io/MLBook/
20 Deep Learning and Scientific Computing with R torch https://skeydan.github.io/Deep-Learning-and-Scientific-Computing-with-R-torch/
21 Efficient Machine Learning with R: Low-Compute Predictive Modeling with tidymodels https://emlwr.org/
22 Explanatory Model Analysis https://pbiecek.github.io/ema/
23 Feature Engineering A-Z https://feaz-book.com/
24 Feature Engineering and Selection: A Practical Approach for Predictive Models http://www.feat.engineering/index.html
25 Hands-On Machine Learning with R https://bradleyboehmke.github.io/HOML/
26 Interpretable Machine Learning https://leanpub.com/interpretable-machine-learning
27 Lightweight Machine Learning Classics with R https://lmlcr.gagolewski.com/
28 Machine Learning-based Causal Inference Tutorial https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/
29 Machine Learning for Factor Investing http://www.mlfactor.com/
30 Mathematics and Programming for Machine Learning with R: From the Ground Up https://www.amazon.com/Mathematics-Programming-Machine-Learning-Ground-ebook-dp-B08JHDCX9Y/dp/B08JHDCX9Y
31 Model-Based Clustering, Classification and Density Estimation Using mclust in R https://mclust-org.github.io/mclust-book/
32 Neural Cryptography Using Keras in R https://www.statswithr.com/neural-cryptography-using-keras-in-r
33 Neural Networks with Keras in R: A QuickStart Guide https://www.statswithr.com/neural-networks-with-keras-in-r-a-quickstart-guide
34 sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series https://e-sensing.github.io/sitsbook/
35 Supervised Machine Learning for Text Analysis in R https://smltar.com/
36 Surrogates – Gaussian Process Modeling, Design and Optimization for the Applied Sciences https://bookdown.org/rbg/surrogates/
37 The caret Package https://topepo.github.io/caret/index.html
38 The Hitchhiker’s Guide to Responsible Machine Learning https://betaandbit.github.io/RML/
39 Tidy Modeling with R https://www.tmwr.org/

📘 Deep Learning

Título Link
1 Deep Learning for the Life Sciences – Ramsundar, Eastman, Walters, Pande [Link]
2 Time Series Analysis & Machine Learning for Predictive Modeling – Rathod et al. [Link]
3 Tutorials on Machine Learning & Deep Learning – Compilação técnica [Link]

📘 LLMs, RAG e IA Generativa

Título Link
1 Unlocking Data with Generative AI and RAG – Keith Bourne [Link]
2 Building LLMs for Production – Sinan Ozdemir [Link]
3 Mastering LLM Applications with LangChain and Hugging Face – Eric Sarrion, Julien Simon [Link]
4 Generative AI for Cloud Solutions Architect – Maddie Schieferstein [Link]

📘 Estatística

🔹 Fundamentos e Introduções

Título Link
1 An Introduction to Statistical and Data Sciences via R https://moderndive.com/
2 An Introduction to Statistical Learning https://www.statlearning.com/
3 Introduction to Modern Statistics https://openintro-ims.netlify.app/
4 OpenIntro Statistics https://leanpub.com/openintro-statistics
5 Statistics (The Easier Way) With R, 3rd Ed. https://amzn.to/3b9ha8s
6 Statistics and Data with R https://www.wiley.com/en-us/Statistics+and+Data+with+R%3A+An+Applied+Approach+Through+Examples-p-9780470758052
7 Answering questions with data https://crumplab.github.io/statistics/

🔹 Bayes e Probabilidade

Título Link
1 Bayes rules! https://www.bayesrulesbook.com/
2 An Introduction to Bayesian Data Analysis for Cognitive Science https://vasishth.github.io/bayescogsci/book/
3 An Introduction to Bayesian Reasoning and Methods https://bookdown.org/kevin_davisross/bayesian-reasoning-and-methods/
4 Doing Bayesian Data Analysis in brms and the tidyverse https://bookdown.org/content/3686/
5 Bayesian analysis of capture-recapture data https://oliviergimenez.github.io/banana-book/index.html
6 Introduction to Empirical Bayes https://drob.gumroad.com/l/empirical-bayes
7 Probability and Bayesian Modeling https://bayesball.github.io/BOOK/probability-a-measurement-of-uncertainty.html
8 Statistical Rethinking https://xcelab.net/rm/statistical-rethinking/
9 Statistical Rethinking with brms, ggplot2, and the tidyverse https://bookdown.org/content/4857/
10 Using R for Bayesian Spatial and Spatio-Temporal Health Modeling https://www.routledge.com/

🔹 Regressão e GLM

Título Link
1 Advanced Regression Methods – Companion to BER642 https://bookdown.org/chua/ber642_advanced_regression/
2 Analysing Data using Linear Models https://bookdown.org/pingapang9/linear_models_bookdown/
3 Beyond Multiple Linear Regression https://bookdown.org/roback/bookdown-BeyondMLR/
4 Flexible Regression Models https://discdown.org/flexregression/
5 Handbook of Regression Modeling in People Analytics http://peopleanalytics-regression-book.org/index.html
6 Introduction to Regression Analysis in R https://www.kellerbiostat.com/introregression/
7 Regression and Other Stories https://avehtari.github.io/ROS-Examples/
8 The Hitchhiker’s Guide to Linear Models https://leanpub.com/linear-models-guide

🔹 Inferência

Título Link
1 Common statistical tests are linear models https://steverxd.github.io/Stat_tests/
2 Statistical inference for data science https://leanpub.com/LittleInferenceBook
3 Foundations of Statistics with R https://mathstat.slu.edu/~speegle/_book/preface.html
4 Statistical Thinking in the 21st Century https://statsthinking21.github.io/statsthinking21-core-site/

🔹 Longitudinal e Mistos

Título Link
1 Applied longitudinal data analysis in brms and the tidyverse https://bookdown.org/content/4253/
2 Mixed Models with R https://m-clark.github.io/mixed-models-with-R/

🔹 Meta-análise e Poder

Título Link
1 Doing meta-analysis with R https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/
2 Power Analysis with Superpower https://aaroncaldwell.us/SuperpowerBook/index.html
3 Introduction to Mediation, Moderation, and Conditional Process Analysis https://bookdown.org/content/b472c7b3-ede5-40f0-9677-75c3704c7e5c/

🔹 Causalidade

Título Link
1 Causal Inference in R https://www.r-causal.org
2 Causal Inference: The Mixtape https://mixtape.scunning.com/
3 The Effect: An Introduction to Research Design and Causality https://theeffectbook.net/

🔹 Séries Temporais e Multivariada

Título Link
1 A Little Book of R for Time Series https://a-little-book-of-r-for-time-series.readthedocs.io
2 A Little Book of R for Multivariate Analysis https://little-book-of-r-for-multivariate-analysis.readthedocs.io

🔹 Didáticos e Catálogos

Título Link
1 End-to-End Solved Problems With R https://amzn.to/2EREAn2
2 Model Estimation by Example https://m-clark.github.io/models-by-example/
3 Library of Statistical Techniques https://lost-stats.github.io/
4 ISLR tidymodels Labs https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html
5 Marginal Effects Zoo https://marginaleffects.com
6 Teacups, Giraffes and Statistics https://tinystats.github.io/teacups-giraffes-and-statistics/index.html
7 The Saga of PLS https://sagaofpls.github.io
8 Translating Stata to R https://stata2r.github.io/

🔹 Áreas Aplicadas

Título Link
1 Building energy statistical modelling https://buildingenergygeeks.org/index.html
2 Modern Statistical Methods for Astronomy https://www.cambridge.org/
3 Spatio-Temporal Statistics with R https://spacetimewithr.org/
4 Surrogates – Gaussian process modeling https://bookdown.org/rbg/surrogates/
5 Partial Least Squares Structural Equation Modeling (PLS-SEM) https://link.springer.com/book/10.1007/978-3-030-80519-7
6 The Grammar of Experimental Designs https://emitanaka.org/edibble-book/index.html
7 One Way ANOVA with R https://bcdudek.net/anova/oneway_anova_basics.pdf
8 Data Analytics https://discdown.org/dataanalytics/
9 R for Data Analytics https://rforanalytics.com/
10 A Business Analyst’s Introduction to Business Analytics https://www.causact.com/

📘 Programação em R

Título Link
1 R para Ciência de Dados (2ª edição) – Português [Link]
2 Ciência de Dados em R – Cursos R [Link]
3 Introdução à Linguagem R: seus fundamentos e sua prática – Pedro Duarte Faria [Link]
4 Statistical Inference via Data Science – Ismay & Kim [Link]
5 ggplot2: Elegant Graphics for Data Science – Hadley Wickham [Link]
6 R for Data Science (1ª ed): Exercise Solutions – Jeffrey B. Arnold [Link]
7 Data Manipulation in R – Steph Locke [Link]